| Name | modularitypruning JSON |
| Version |
1.4.1
JSON |
| download |
| home_page | https://github.com/ragibson/ModularityPruning |
| Summary | Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. |
| upload_time | 2024-10-29 03:05:25 |
| maintainer | None |
| docs_url | None |
| author | Ryan Gibson |
| requires_python | <4,>=3.8 |
| license | None |
| keywords |
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| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# ModularityPruning
ModularityPruning is a pruning tool to identify small subsets of network
partitions that are significant from the perspective of stochastic block model
inference. This method works for single-layer and multi-layer networks, as well
as for restricting focus to a fixed number of communities when desired.
See the [documentation](https://modularitypruning.readthedocs.io/en/latest/)
or the journal article at https://doi.org/10.1038/s41598-022-20142-6 for more
information.
Significantly more details can be found in the article's
[Supplementary Information](
https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-022-20142-6/MediaObjects/41598_2022_20142_MOESM1_ESM.pdf
).
## Installation
This project is on [PyPI](https://pypi.org/project/modularitypruning/) and can
be installed with
pip install modularitypruning
# OR
pip3 install modularitypruning
Alternatively, you can install it from this repository directly:
git clone https://github.com/ragibson/ModularityPruning
cd ModularityPruning
python3 setup.py install
<a name = "Basic Usage"></a>
## Basic Usage
This package interfaces directly with python-igraph. A simple example of its
usage is
```python
import igraph as ig
from modularitypruning import prune_to_stable_partitions
from modularitypruning.leiden_utilities import repeated_leiden_from_gammas
import numpy as np
# get Karate Club graph in igraph
G = ig.Graph.Famous("Zachary")
# run leiden 1000 times on this graph from gamma=0 to gamma=2
partitions = repeated_leiden_from_gammas(G, np.linspace(0, 2, 1000))
# prune to the stable partitions from gamma=0 to gamma=2
stable_partitions = prune_to_stable_partitions(G, partitions, 0, 2)
print(stable_partitions)
```
This prints
[(0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 1, 0, 2, 0, 2, 0, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2)]
which is the stable 4-community split of the Karate Club network.
## More Information
The [issues (which contains some potential future work)](https://github.com/ragibson/ModularityPruning/issues)
and [figure generation runtimes README](figure_generation_runtimes.md) may also
be of interest.
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"description": "# ModularityPruning\n\n\n\nModularityPruning is a pruning tool to identify small subsets of network\npartitions that are significant from the perspective of stochastic block model\ninference. This method works for single-layer and multi-layer networks, as well\nas for restricting focus to a fixed number of communities when desired.\n\nSee the [documentation](https://modularitypruning.readthedocs.io/en/latest/)\nor the journal article at https://doi.org/10.1038/s41598-022-20142-6 for more\ninformation.\n\nSignificantly more details can be found in the article's\n[Supplementary Information](\nhttps://static-content.springer.com/esm/art%3A10.1038%2Fs41598-022-20142-6/MediaObjects/41598_2022_20142_MOESM1_ESM.pdf\n).\n\n## Installation\n\nThis project is on [PyPI](https://pypi.org/project/modularitypruning/) and can\nbe installed with\n\n pip install modularitypruning\n # OR\n pip3 install modularitypruning\n\nAlternatively, you can install it from this repository directly:\n\n git clone https://github.com/ragibson/ModularityPruning\n cd ModularityPruning\n python3 setup.py install\n\n<a name = \"Basic Usage\"></a>\n\n## Basic Usage\n\nThis package interfaces directly with python-igraph. A simple example of its\nusage is\n\n```python\nimport igraph as ig\nfrom modularitypruning import prune_to_stable_partitions\nfrom modularitypruning.leiden_utilities import repeated_leiden_from_gammas\nimport numpy as np\n\n# get Karate Club graph in igraph\nG = ig.Graph.Famous(\"Zachary\")\n\n# run leiden 1000 times on this graph from gamma=0 to gamma=2\npartitions = repeated_leiden_from_gammas(G, np.linspace(0, 2, 1000))\n\n# prune to the stable partitions from gamma=0 to gamma=2\nstable_partitions = prune_to_stable_partitions(G, partitions, 0, 2)\nprint(stable_partitions)\n``` \n\nThis prints\n\n [(0, 0, 0, 0, 1, 1, 1, 0, 2, 2, 1, 0, 0, 0, 2, 2, 1, 0, 2, 0, 2, 0, 2, 3, 3, 3, 2, 3, 3, 2, 2, 3, 2, 2)]\n\nwhich is the stable 4-community split of the Karate Club network.\n\n## More Information\n\nThe [issues (which contains some potential future work)](https://github.com/ragibson/ModularityPruning/issues)\nand [figure generation runtimes README](figure_generation_runtimes.md) may also\nbe of interest.\n",
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